Published online 21 November 2011
Nucleic Acids Research, 2012, Vol. 40, Database issue D549–D553
doi:10.1093/nar/gkr1049
ProtChemSI: a network of protein–chemical
structural interactions
Olga V. Kalinina1,2, Oliver Wichmann1, Gordana Apic1,3 and Robert B. Russell1,*
1
Cell Networks, BioQuant, University of Heidelberg, Im Neuenheimer Feld 267, 69120 Heidelberg, Germany,
Institute for Information Transmission Problems, RAS, Bolshoi Karenty pereulok 19, Moscow, 127994, Russia
and 3Cambridge Cell Networks Ltd, St John’s Innovation Centre, Cowley Road, Cambridge CB4 OWS, UK
2
Received August 15, 2011; Revised October 4, 2011; Accepted October 25, 2011
ABSTRACT
Progress in structure determination methods means
that the set of experimentally determined 3D structures of proteins in complex with small molecules is
growing exponentially. ProtChemSI exploits and
extends this useful set of structures by both collecting and annotating the existing data as well as
providing models of potential complexes inferred
by protein or chemical structure similarity. The
database currently includes 7704 proteins from
1803 organisms, 11 324 chemical compounds and
202 289 complexes including 178 974 predicted. It
is publicly available at http://pcidb.russelllab.org.
INTRODUCTION
Protein–chemical interactions are most often not considered in the context of three-dimensional (3D) structures. Most databases, such as DrugBank (1) or
STITCH (2) will refer to 3D structures but do not exploit
them beyond reporting that a structure for a drug–protein
interaction is known. Other databases, such as Binding
MOAD (3), PDBbind (4) and BindingDB (5), focus on
collecting protein–ligand complexes, but report only
those that are experimentally resolved. However, the current network of protein–chemical interactions derived
from 3D structures is a rich source of information and provides many possibilities to suggest new protein–chemical
interactions.
Recently, we published a method to predict novel protein–chemical interactions using superimposition of
known 3D structures (6). The underlying principle is
that if two proteins share a common ligand, and the first
protein is known to bind a second ligand, the 3D structures of protein–ligand complexes can be superimposed to
build a model that can be used to evaluate a complex of
the second protein with that second ligand (Figure 1,
lower). Here we present ProtChemSI, a database
providing these computed complexes. The database also
contains known structures of protein–chemical complexes,
and several other predicted complexes. Specifically, we
also construct models for all interactions with molecules
similar to known interaction partners of a protein or a
chemical of interest (Figure 1, explained in detail below),
and provide a method to traverse the network of interactions to identify possibilities for building a structural
model of any protein chemical pair of interest (Figure 2).
Being primarily based on structural interactions,
ProtChemSI has little overlap with other databases for
protein–chemical interactions, such as DrugBank (1),
STITCH (2) and ChEMBL (7) (Table 1). Theoretically,
protein–chemical interactions viewed as a network provide
a possibility to construct a model of a complex of any
given protein and chemical, superimposing molecules
along the path that connects them. ProtChemSI implements a routine to construct and evaluate these models
on user demand, so the total number of theoretically
possible models in ProtChemSI is very large and impossible to quantify. However, including first-order models
(i.e. where we consider interactions no more than twosteps away in the network), we have a total of 23 315
known complexes, and predictions, where 65 502 are
modeled by obvious homology, 18 917 are modeled by
obvious chemical similarity and 94 555 are modeled by
superimpositions as detailed in our original study (6).
FUNCTIONS OF THE DATABASE
ProtChemSI is intended for those interested in structural
details of interactions between proteins and small molecules. It provides details at two levels of certainty: first,
it lists all experimentally resolved 3D structures involving
the query protein or chemical; second, it constructs a
number of models as detailed below.
The workflow of the model construction is schematically represented in Figure 1. For a query protein, models
of the following complexes are constructed: (i) with
*To whom correspondence should be addressed. Tel: +49 6221 54 51 362; Fax: +49 6221 54 51 486; Email: [email protected]
Present address:
Olga V. Kalinina, Max-Planck-Institut für Informatik, Campus E1 4, 66123 Saarbrücken, Germany.
ß The Author(s) 2011. Published by Oxford University Press.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/
by-nc/3.0), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
D550 Nucleic Acids Research, 2012, Vol. 40, Database issue
Figure 1. Schematic representation of the construction of the models of protein–ligand complexes. The models are constructed using superimposition
of similar chemicals (top-left corner), superimposition of similar protein (top-right corner) or double superimposition of identical proteins and
chemicals in the case when two proteins share a common ligand and one of them has another ligand (bottom).
chemicals that are similar to experimentally observed
binding ligands (Figure 1, upper left); (ii) with chemicals
that bind to proteins similar to the query (Figure 1, upper
right); (iii) with chemicals that bind to another protein
that shares at least one binding ligand with the query
(Figure 1, bottom). The models for chemical queries are
constructed analogously. All the models are scored according to statistics described in (6). Briefly, a number
of physical and chemical parameters of the complex are
combined to give a score from 0 to 7, with 7 being the best
possible model. Models with a P 0.05 (which corresponds to a score of 5.6), i.e. having a 5% chance of
being randomly generated, are highlighted for the user.
Another application within ProtChemSI is a shortestpath and superimposition functionality. When given a
protein and a chemical, this feature attempts to construct
a complex of them by first finding the shortest path
between the molecules through the network of experimentally determined structures. It then sequentially superimposes them using the similar components of two adjacent
complexes as templates, similar to Figure 1, to obtain a
complex of the two components of interest. Then it evaluates them and reports as described above.
An example of this shortest path reconstruction is presented in Figure 2. We can construct a complex of the
well-documented (1) interaction between the human
Nucleic Acids Research, 2012, Vol. 40, Database issue
D551
Figure 2. Example of reconstruction of a known interaction. Mineralocorticoid receptor is documented as a target for fludrocortisone in the
DrugBank (1). ProtChemSI reconstruct the complex corresponding to this interaction either using superimposition of similar proteins (left part)
of a chain of superimpositions of protein–ligand complexes that constitute the shortest path in the network between Mineralocorticoid receptor and
fludrocortisone (right part). The main chain of the protein is shown in green, the ligand is shown in sticks mode. The contacting atoms of the protein
are shown in ball-and-stick mode and colored by atom type with gray carbons.
Table 1. Overlap with other sources for protein-chemical interactions
DrugBank
STITCH
ChEMBL
Number of
proteins
Number of
chemicals
Number of
protein–chemical
direct complexes/
interactions
Number
of models
2311
3091
1006
1944
2661
2083
2770
1861
1324
1010
957
3038
Mineralocorticoid receptor and fludrocortisone in two
ways. First, we get a complex by superimposing the 3D
structure of the Mineralocorticoid receptor onto the structure of the homologous human Androgen receptor complexed with fludrocortisone, with a highly significant score
of 6.43. The second route is to do a chain of superimpositions of the complexes of Mineralocorticoid receptor with
progesterone, progesterone with Progesterone receptor,
Progesterone
receptor
with
methyltrienolone,
methyltrienolone with Androgen receptor and Androgen
receptor with fludrocortisone, also giving a highly significant score of 6.42.
CONTENT OF THE DATABASE
The database of ProtChemSI rests on two main concepts:
molecules and links. Molecules are proteins and chemicals
[defined as anything with a corresponding entry in
PubChem (8)] that appear in the Protein Data Bank (9)
to be in contact in an X-ray or NMR resolved 3D structure. Links represent either complexes of a protein and a
chemical or a similarity relationship between two proteins
or two chemicals. Two proteins are considered similar if
they are >30% identical in a BLAST (10) search with
E-value < 0.01, and two chemicals, if the Tanimoto score
[using PubChem fingerprints (11)] between them is 0.9.
Chemicals with less than five heavy atoms are currently
ignored. We also filter out solvents, buffer components
and other non-specifically binding ligands. To do this,
we inspected all ligands with more than 20 linked
proteins manually and filtered those belonging to one of
these categories. Chlorophyll, heme and other porphyrins
were also removed, as they represent a very specific case of
protein–ligand interaction that tend to obscure more
specific interactions made by smaller ligands.
For each pair of instances of the same molecule, or
similar molecules, a transformation matrix bringing
them into the same frame of reference is also stored.
The models that are being reported are not stored explicitly, but are calculated on-demand. The recently
computed models are cached and kept for 2 weeks.
The database is updated monthly. The flat-files of the
current release can be downloaded from the website.
WEB INTERFACE
There are two main routes to interact with ProtChemSI:
browsing the database, or searching by protein name,
UniProt ID, protein sequence, ligand name, PubChem
ID or SMILES string. For any given protein or
chemical, a number of features are shown: (i) known
binding chemicals/proteins from the experimentally
resolved 3D structures; (ii) possible interacting chemicals/proteins that are similar to those bound by the
query and form plausible models based on the statistics
described above; (iii) possible interacting chemicals/
proteins that bind molecules similar to the query and
form plausible models; and (iv) possible interacting chemicals/proteins from all one-step models as represented in
Figure 1. It is possible to select a second molecule in the
network and attempt to construct a model of the complex
of the two, using the chain of superimpositions, as
described above.
D552 Nucleic Acids Research, 2012, Vol. 40, Database issue
Figure 3. (A) Model of Captopril with its natural target, human angiotensin I-converting enzyme (model score 5.78, highly significant). (B) Model of
Sorafenib with its known (VEGF receptor 2, Kit kinase) and potential (Lck and Src kinases, hepatocyte growth factor receptor) targets.
Representation and coloring scheme same as in Figure 2.
USE CASES
The database can provide interesting suggestions of new
compounds binding to proteins, particularly when several
protein–complex structures have already been determined.
For instance, when one searches for interactions involving
human carbonic anhydrase 2 (CAH2), a protein with
many known chemical inhibitors, the server first reports
133 complexes of known structure, 11 complexes predicted
based on homologous proteins (of which 3 are significant),
4 predictions (none significant) based on similar chemicals
and 44 predictions (2 significant) based on protein/ligand
superimpositions as described in the original paper.
Among these are several somewhat obvious predictions
(e.g. where a close homolog is used to make the prediction), but also some intriguing suggestions such as hydroxyaminovaline, which binds to Collagenase 3 (MMP13)
which in turn shares the bound chemical acetohydroxamate with CAH2. This prediction is structurally plausible,
with the additional propyl group in hydroxaminovaline
relative to acetohydroxamate fitting nicely into a hydrophobic pocket, however to our knowledge this compound
is not currently known to affect CAH2.
The database can also provide structures for known
protein–chemical interactions lacking an experimental
structure. We tested this by inspecting all interactions
within Drugbank (1). There are 2785 experimentally
resolved 3D structures of DrugBank drug–target interactions within our database. Additionally, we construct
1100 models involving drugs and their targets. For
example, for a specific inhibitor of angiotensin
I-converting enzyme (ACE), Captopril, which is used to
treat hypertension, only its complex with the Drosophila
ortholog is resolved (PDB code 1J37). Despite low identity
to the human protein (45%), we are able to reconstruct confident models with its cognate human target
(Figure 3A). Another interesting example is Sorafenib,
an anti-cancer drug that is reported to bind a number of
protein kinases. Using resolved structures with two of
them, MAPK14 (PDB codes 3GSC, 3HEG) and B-raf
(PDB codes 1UWJ, 1UWH), we reconstruct models with
known targets VEGF receptor 2 and Kit kinase, as well
as with a number of novel potential targets, Lck and
Src kinases and hepatocyte growth factor receptor
(Figure 3B).
It is also possible to find new suggestions for wellstudied chemicals such as metabolites. For instance, using
cholesterol as a query, one obtains, among others, its
model with an orphan receptor LXR-b (using similarity
to ROR-g) or with a yeast oxystrerol-binding protein
KES1 (using 1-step superimposition via NPC1 and
25-hydroxycholesterol). Even some of the most wellstudied drugs have some intriguing findings, for instance,
Aspirin is predicted with confidence to bind to
b-galactosidase from Escherichia coli, which is not the
case for other non-steroidal anti-inflammatory agents of
the same class, such as diclofenac or acetaminophen/
paracetamol, a result which agrees broadly with a study
of the effect of these chemicals on the human enzyme
(12,13).
CONCLUSION
The database provides a view of protein–chemical interactions from a structural perspective. Although these data
are currently growing exponentially, the structural angle is
often missing from other resources. We provide structural
models for well-known interactions and provide putative
models for millions of others. The database provides an
excellent complement to existing tools to study protein–
chemical interactions. Due to the nature of the method
behind the database, both the number and quality of
models in ProtChemSI will increase as the set of experimentally determined complexes grows. We anticipate that
the database will play as important a role in the study of
Nucleic Acids Research, 2012, Vol. 40, Database issue
protein–ligand interactions as homology modeling plays
in structural biology in general.
ACKNOWLEDGEMENTS
The authors are grateful to Dr Matthew J. Betts and Dr
Chad A. Davis for the technical support and the critical
reading of the manuscript.
FUNDING
This work was funded under the Excellence Initiative
‘‘Cell Networks’’ from the German Science Ministry
(DFG). Funding for open access charge: DFG.
Conflict of interest statement. None declared.
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